Advancing global sea ice prediction capabilities using a fully-coupled climate model with integrated machine learning
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arXiv
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| Format: | Preprint |
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2025
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| author | Gregory, William Bushuk, Mitchell Zhang, Yong-Fei Adcroft, Alistair Zanna, Laure McHugh, Colleen Jia, Liwei |
| author_facet | Gregory, William Bushuk, Mitchell Zhang, Yong-Fei Adcroft, Alistair Zanna, Laure McHugh, Colleen Jia, Liwei |
| contents | We showcase a hybrid modeling framework which embeds machine learning (ML) inference into the GFDL SPEAR climate model, for online sea ice bias correction during a set of global fully-coupled 1-year retrospective forecasts. We compare two hybrid versions of SPEAR to understand the importance of exposing ML models to coupled ice-atmosphere-ocean feedbacks before implementation into fully-coupled simulations: Hybrid_CPL (with feedbacks) and Hybrid_IO (without feedbacks). Relative to SPEAR, Hybrid_CPL systematically reduces seasonal forecast errors in the Arctic and significantly reduces Antarctic errors for target months May-December, with >2x error reduction in 4-6-month lead forecasts of Antarctic winter sea ice extent. Meanwhile, Hybrid_IO suffers from out-of-sample behavior which can trigger a chain of Southern Ocean feedbacks, leading to ice-free Antarctic summers. Our results demonstrate that ML can significantly improve numerical sea ice prediction capabilities and that exposing ML models to coupled ice-atmosphere-ocean processes is essential for generalization in fully-coupled simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18328 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Advancing global sea ice prediction capabilities using a fully-coupled climate model with integrated machine learning Gregory, William Bushuk, Mitchell Zhang, Yong-Fei Adcroft, Alistair Zanna, Laure McHugh, Colleen Jia, Liwei Atmospheric and Oceanic Physics We showcase a hybrid modeling framework which embeds machine learning (ML) inference into the GFDL SPEAR climate model, for online sea ice bias correction during a set of global fully-coupled 1-year retrospective forecasts. We compare two hybrid versions of SPEAR to understand the importance of exposing ML models to coupled ice-atmosphere-ocean feedbacks before implementation into fully-coupled simulations: Hybrid_CPL (with feedbacks) and Hybrid_IO (without feedbacks). Relative to SPEAR, Hybrid_CPL systematically reduces seasonal forecast errors in the Arctic and significantly reduces Antarctic errors for target months May-December, with >2x error reduction in 4-6-month lead forecasts of Antarctic winter sea ice extent. Meanwhile, Hybrid_IO suffers from out-of-sample behavior which can trigger a chain of Southern Ocean feedbacks, leading to ice-free Antarctic summers. Our results demonstrate that ML can significantly improve numerical sea ice prediction capabilities and that exposing ML models to coupled ice-atmosphere-ocean processes is essential for generalization in fully-coupled simulations. |
| title | Advancing global sea ice prediction capabilities using a fully-coupled climate model with integrated machine learning |
| topic | Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2505.18328 |